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added experimental face type 'whole_face'

Browse source 
Basic usage instruction: https://i.imgur.com/w7LkId2.jpg

	'whole_face' requires skill in Adobe After Effects.

	For using whole_face you have to extract whole_face's by using
	4) data_src extract whole_face
	and
	5) data_dst extract whole_face
	Images will be extracted in 512 resolution, so they can be used for regular full_face's and half_face's.

	'whole_face' covers whole area of face include forehead in training square,
	but training mask is still 'full_face'
	therefore it requires manual final masking and composing in Adobe After Effects.

added option 'masked_training'
	This option is available only for 'whole_face' type.
	Default is ON.
	Masked training clips training area to full_face mask,
	thus network will train the faces properly.
	When the face is trained enough, disable this option to train all area of the frame.
	Merge with 'raw-rgb' mode, then use Adobe After Effects to manually mask, tune color, and compose whole face include forehead.
This commit is contained in:
Colombo 2020-02-21 16:21:04 +04:00
parent 778fb94246
commit f1d115b63b
10 changed files with 74 additions and 58 deletions
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5
mainscripts/Extractor.py
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@ -680,7 +680,6 @@ def main(detector=None,
manual_fix=False,
manual_output_debug_fix=False,
manual_window_size=1368,
image_size=256,
face_type='full_face',
max_faces_from_image=0,
cpu_only = False,
@ -688,6 +687,8 @@ def main(detector=None,
):
face_type = FaceType.fromString(face_type)
image_size = 512 if face_type == FaceType.WHOLE_FACE else 256
if not input_path.exists():
io.log_err ('Input directory not found. Please ensure it exists.')
return
@ -710,7 +711,7 @@ def main(detector=None,
if not manual_output_debug_fix and input_path != output_path:
output_images_paths = pathex.get_image_paths(output_path)
if len(output_images_paths) > 0:
io.input(f"WARNING !!! \n {output_path} contains files! \n They will be deleted. \n Press enter to continue.")
io.input(f"\n WARNING !!! \n {output_path} contains files! \n They will be deleted. \n Press enter to continue.\n")
for filename in output_images_paths:
Path(filename).unlink()
else: